• 천문학회지
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• 제56권2호
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• pp.213-224
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• 2023

Type II solar radio bursts show frequency drifts from high to low over time. They have been known as a signature of coronal shock associated with Coronal Mass Ejections (CMEs) and/or flares, which cause an abrupt change in the space environment near the Earth (space weather). Therefore, early detection of type II bursts is important for forecasting of space weather. In this study, we develop a deep-learning (DL) model for the automatic detection of type II bursts. For this purpose, we adopted a 1-D Convolution Neutral Network (CNN) as it is well-suited for processing spatiotemporal information within the applied data set. We utilized a total of 286 radio burst spectrum images obtained by Hiraiso Radio Spectrograph (HiRAS) from 1991 and 2012, along with 231 spectrum images without the bursts from 2009 to 2015, to recognizes type II bursts. The burst types were labeled manually according to their spectra features in an answer table. Subsequently, we applied the 1-D CNN technique to the spectrum images using two filter windows with different size along time axis. To develop the DL model, we randomly selected 412 spectrum images (80%) for training and validation. The train history shows that both train and validation losses drop rapidly, while train and validation accuracies increased within approximately 100 epoches. For evaluation of the model's performance, we used 105 test images (20%) and employed a contingence table. It is found that false alarm ratio (FAR) and critical success index (CSI) were 0.14 and 0.83, respectively. Furthermore, we confirmed above result by adopting five-fold cross-validation method, in which we re-sampled five groups randomly. The estimated mean FAR and CSI of the five groups were 0.05 and 0.87, respectively. For experimental purposes, we applied our proposed model to 85 HiRAS type II radio bursts listed in the NGDC catalogue from 2009 to 2016 and 184 quiet (no bursts) spectrum images before and after the type II bursts. As a result, our model successfully detected 79 events (93%) of type II events. This results demonstrates, for the first time, that the 1-D CNN algorithm is useful for detecting type II bursts.

• 한국신재생에너지학회:학술대회논문집
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• 한국신재생에너지학회 2010년도 춘계학술대회 초록집
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• pp.126.1-126.1
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• 2010

In direct methanol fuel cells(DMFCs), it is well known that methanol crossover severely reduces the cell performance and the cell efficiency. There are a number of design and operating parameters that influence the methanol crossover. This indicates that a DMFC demands a high degree of optimization. For the successful design and operation of a DMFC system, a better understanding of methanol crossover phenomena is essential. The main objective of this study is to examine methanol-crossover phenomena in DMFCs. In this study, 1D DMFC model previously developed by Ko et al. is used. The simulation results were compared with methanol-crossover data that were measured by Eccarius et al. The numerical predictions agree well with the methanol crossover data and the model successfully captures key experimental trends.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2009년 추계학술대회논문집
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• pp.177-184
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• 2009

Cavitating flow simulation is of practical importance for many engineering systems, such as marine propellers, pump impellers, nozzles, injectors, torpedoes, etc. The present work has focused on the simulation of cavitating flow past cylinders with strong side flows. The governing equation is the Navier-Stokes equation based on the homogeneous mixture model. The momentum and energy equation is in the mixture phase while the continuity equation is solved liquid and vapor phase, separately. An implicit dual time and preconditioning method are employed for computational analysis. For the code validation, the results from the present solver have been compared with experiments and other numerical results. A fairly good agreement with the experimental data and other numerical results have been obtained. After the code validation, the strong side flow was applied to include the wake flow effects of the submarine or ocean tide.

• 대한기계학회논문집A
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• 제33권9호
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• pp.922-929
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• 2009

This paper deals with the State-Dependent Riccati Equation (SDRE) Technique for the design of helicopter nonlinear waypoint guidance controller. To generate the flight guidance through multiple waypoints, we use the trigonometric spline. The controller design and its validation is based upon a level 2 simulation helicopter model and the designed SDRE controller is applied to the trajectory tracking problems. To validate the designed SDRE controller, the simulation environment of high fidelity helicopter model is developed using three independent computers. This paper focuses on the validation the present SDRE controller through the helicopter waypoint guidance simulation.

• International Journal of Aeronautical and Space Sciences
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• 제10권2호
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• pp.12-22
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• 2009

This paper deals with the State-Dependent Riccati Equation (SDRE) Technique for the design of rotorcraft waypoint guidance. To generate the flight trajectory through multiple waypoints, we use the trigonometric spline. The controller design and its validation is based upon a level 2 simulation rotorcraft model and the designed SDRE controller is applied to the trajectory tracking problems. To verify the designed guidance law, the simulation environment of high fidelity rotorcraft model is developed using three independent PCs. This paper focuses on the validation of rotorcraft waypoint guidance law which is designed by using SDRE Controller.

• 대한인간공학회지
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• 제18권3호
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• pp.153-169
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• 1999

The design of nuclear control room is advancing toward totally computer based human system interfaces (HSI). Computer based interfaces offer the opportunity to provide improved support of operator performance, but if not properly deployed, can introduce new challenges. This paper reviews the Westinghouse AP-600 Human Factors Verification and Validation Plan selected for HSI evaluation model of Korea next generation nuclear control rooms. The AP-600 HSI evaluation model addressed 15 evaluation issues considering major activity class of operator and task complexity factors. This paper also describes the test procedures experimenters should follow to evaluate the addressed issues.

• Journal of the Korean Data and Information Science Society
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• 제28권1호
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• pp.227-235
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• 2017

When the spatial information of each location is given specifically as coordinates it is popular to use the geographically weighted regression to incorporate the spatial information by assuming that the regression parameters vary spatially across locations. In this paper, we relax the linearity assumption of geographically weighted regression and propose a geographically weighted least squares-support vector machine for estimating geographically weighted mean by using the basic concept of kernel machines. Generalized cross validation function is induced for the model selection. Numerical studies with real datasets have been conducted to compare the performance of proposed method with other methods for predicting geographically weighted mean.

• Journal of electromagnetic engineering and science
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• 제13권3호
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• pp.192-194
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• 2013

This paper proposes a validation model using a reverberation chamber for verification of the effectiveness and accuracy of the statistical power balance (PWB) method. The PWB method treats electromagnetic (EM) field distributions in certain environments as a representative value of an "ensemble" average, so a reverberation chamber is utilized as a testing apparatus to experimentally simulate the sets of measurement field "ensemble" inside a complex environment. Measurements are performed with various test conditions for environmental loss using flat absorbers, and front and side doors of the chamber. Comparison between the PWB analysis and the measurement results shows good agreement, indicating the usefulness of this statistical analysis method for stating the electromagnetic field inside the EM zone as a representative value to general structures.

• 약학회지
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• 제46권5호
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• pp.331-336
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• 2002

The purpose of this study is to develop a non-invasive blood glucose measurement method by a portable near infrared (NIR) system which was newly integrated by our lab. The portable NIR system includes a tungsten halogen lamp, a specialized reflectance fiber optic probe and a photo diode array type InGaAs detector; which was developed by a microchip technology based on the lithography. Reflectance NIR spectra of different parts of human body (finger tip, earlobe, and inner lip) were recorded by using a fiber optic probe. The spectra were collected over the spectral range 1100 ∼ 1740 nm. Partial least squares regression (PLSR) was applied for the calibration and validation for the determination of blood glucose. The calibration model from earlobe spectra presented better results, showing good correlation with a glucose oxidase method which is a mostly used standard method. This model predicted the glucose concentration for validation set with a SEP of 33 mg/dL. This study indicated the feasibility for non-invasive monitoring of blood glucose by a portable near infrared system.

• Journal of the Korean Data and Information Science Society
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• 제21권6호
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• pp.1319-1325
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• 2010

Quantile regression provides a more complete statistical analysis of the stochastic relationships among random variables. Sometimes quantile functions estimated at different orders can cross each other. We propose a new non-crossing quantile regression method applying support vector median regression to restricted regression quantile, restricted support vector quantile regression. The proposed method provides a satisfying solution to estimating non-crossing quantile functions when multiple quantiles for high dimensional data are needed. We also present the model selection method that employs cross validation techniques for choosing the parameters which aect the performance of the proposed method. One real example and a simulated example are provided to show the usefulness of the proposed method.